Eeg recording and analysis

ABSTRACT

One embodiment provides a method, including: obtaining EEG data from one or more single channel EEG sensor worn by a user; classifying, using a processor, the EEG data as one of nominal and abnormal; and providing an indication associated with a classification of the EEG data. Other embodiments are described and claimed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. provisional patent applicationSer. No. 63/005,405, filed on Apr. 5, 2020 and having the same title,the contents of which are incorporated by reference in their entiretyherein.

BACKGROUND

There are thousands of hospitals across the U.S. Many of these hospitalsare community or rural hospitals. These community or rural hospitalsconventionally are part of a hospital system or network. An example ofone such network includes several community hospitals with one majortertiary hospital. A community or rural hospital outside of any largehospital network would typically contract with a large tertiary hospitalfor emergent and intensive-care solutions outside of the areas ofexpertise of the community or rural hospital.

Electroencephalogram (EEG) monitoring is conventionally only availablein the large tertiary hospitals that support a neurology department withan EEG service. Many hospitals do not offer EEG monitoring. Thesehospitals make arrangements with larger tertiary hospitals or theirpartners when such monitoring is required or desirable for patients.This conventionally takes the form of a referral of the patient to thetertiary hospital for expert of specialist services. Often this includestravel or transport of the patient to the tertiary hospital forservices.

BRIEF SUMMARY

In summary, one embodiment provides a method, comprising: obtaining EEGdata from one or more single-channel EEG sensor worn by a user;classifying, using a processor, the EEG data as one of nominal andabnormal; and providing an indication associated with a classificationof the EEG data.

Another embodiment provides a system, comprising: an output device; aprocessor operatively coupled to the output device; and a memory storinginstructions executable by the processor to: obtain EEG data from one ormore single-channel EEG sensor worn by a user; classify the EEG data asone of nominal and abnormal; and provide an indication associated with aclassification of the EEG data.

A further embodiment provides a method, comprising: obtaining EEG datafrom two or more single channel EEG sensors worn by a user; transmittingthe EEG data to a remote device; and providing, from the remote deviceto a display associated with a remote user, data comprising a montage ofthe EEG data.

The foregoing is a summary and thus may contain simplifications,generalizations, and omissions of detail; consequently, those skilled inthe art will appreciate that the summary is illustrative only and is notintended to be in any way limiting.

For a better understanding of the embodiments, together with other andfurther features and advantages thereof, reference is made to thefollowing description, in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1A illustrates an example sensor.

FIG. 1B illustrates an example sensor.

FIG. 2 illustrates an example system.

FIG. 3 illustrates an example of EEG monitoring and indicating.

FIG. 4A and FIG. 4B illustrate example EEG data.

FIG. 5 illustrates an example of EEG data classification.

FIG. 6 illustrates an example method of EEG monitoring and indicating.

FIG. 7A and FIG. 7B illustrate example application views or screens.

FIG. 8 illustrates an example system.

DETAILED DESCRIPTION

It will be readily understood that the components of the embodiments, asgenerally described and illustrated in the figures herein, may bearranged and designed in a wide variety of different configurations inaddition to the described example embodiments. Thus, the following moredetailed description of the example embodiments, as represented in thefigures, is not intended to limit the scope of the claims, but is merelyrepresentative of those embodiments.

To optimize treatment following initial seizure diagnosis,epileptologists would ideally obtain high-quality, long-term EEG studiesin the hospital with 19+-channel, wired EEG arrangement in the standardinternational ‘10-20’ system. Such studies can be difficult to performbecause the process is prohibitively expensive, time consuming, andextremely inconvenient for patients. Additionally, the time spent in theepilepsy monitoring unit may not capture any or all seizure activitythat a person normally has over long periods of time. However, optimaltreatment often depends upon identifying the full extent of a patient'sconvulsive (clinical) and non-convulsive (sub-clinical) seizureactivity. Currently a technique for collecting seizure activity historyoutside of the hospital is the seizure diary—a home based, self-reportedrecord that may be incomplete. Seizure diaries can be difficult tomaintain accurately, and the diary can be inaccurate, making clinicaldecisions on appropriate pharmacological treatment difficult. Further,conventional EEG techniques do not adequately address the need formonitoring in many emergent care scenarios, particularly thoseencountered by emergency responders or clinicians in small or ruralhospitals.

Turning now to the figures, representative example embodiments aredescribed to provide a better understanding of the appended claims.

Referring to FIG. 1A, an embodiment provides a sensor 101 a. The sensor101 a may take a variety of forms; however, the example of FIG. 1A showsa wearable sensor that adheres to the skin of a patient. The sensor 101a may include a data analytics platform that takes EEG monitoring out ofthe hospital. The sensor 101 a features a small size and can be locatedin a variety of positions, reducing the burden on the user duringprolonged wear. This permits EEG data collection using a small,convenient sensor 101 a that provides real-time EEG data capture, asshown in FIG. 1A. This is in contrast to a complex wired electrodearrangement typically used in a hospital setting featuring manyelectrodes and measurement locations.

In an example shown in FIG. 1A, a small, connected health-wearablesensor 101 a provides for sensing single-channel EEG data that can beanalyzed for occurrences of both convulsive (clinical) andnon-convulsive (subclinical) seizures. The sensor 101 a senses EEG dataof the user by detecting electrical activity of the brain, e.g., bysensing voltage differentials between two electrode contacts. The sensormay include two electrodes that are separated by the sensor housing,forming a single bipolar channel (“single channel”) of a typical EEGmontage. In an embodiment, the sensor 101 a is a single-channeldifferentially-amplified transmitter and data logger with 6 mm diametergold electrodes and 18 mm electrode spacing, similar to a bipolar pairin high-density EEG. The electrode contacts may be located on thesurface of the device that is placed on the skin of the patient, e.g.,adhered to the patient via a sticker or other adhesive that includesmaterial such as hydrogel permitting voltage sensing. The sensor 101 ais located at an appropriate place on the user, e.g., on the scalp belowthe hairline, in order to sense and record the single channel EEG data.The EEG data may be analyzed on-board, e.g., via application of ananalysis or machine learning model stored in the sensor 101 a or may beanalyzed by a local device or remote device or a combination of theforegoing. By way of example, the sensor 101 a may communicate through awireless network, e.g. secure BLUETOOTH Low Energy (BLE), to a localdevice using a personal area network (PAN), such as communicating datato a smartphone or a tablet. Similarly, the sensor 101 a may communicatewith a remote device using a wide area network (WAN), such ascommunicating EEG data to a remote server or cloud device over theInternet, with or without communicating via an intermediary device suchas a local device.

In an embodiment, an EEG monitoring sensor 101 a is a self-containedrecording patch including a first electrode and a second electrode,where the first and second electrodes cooperate to measure a voltage.The sensor 101 a includes circuitry for generating an EEG signal fromthe measured voltage, amplifying the EEG signal, digitizing the EEGsignal, and retrievably storing the EEG data in a memory. The sensor 101a may also include a power source and an enclosure that houses thecircuitry, the power source, as well as the first and second electrodes,in a unitary package. The sensor 101 a may be worn on the scalp, e.g.,forehead or bi-parietal region, of the user to capture EEG data over along period as the user goes about his or her regular, daily activities.

As illustrated in FIG. 1B, in an embodiment, the sensor 101 b isdesigned to be discreet and water-resistant, allowing for continuous usein all facets of a person's normal daily life. In the example of FIG. 2,the sensor 101 b may be located below the hairline in a location such asbehind the ear. The placement of the sensor 101 b may be aided by aprior diagnosis, e.g., following a formal evaluation using a fullmontage of sensors (via monitoring with a typical wired sensor array ina cap) or via refined location estimates facilitated by adjusting thelocation of the sensor 101 b or a set of sensors over time. By choosingan appropriate placement for the sensor 101 b, the usefulness of singlechannel EEG data is improved by locating the sensor 101 b proximate tobrain activity or focus of seizure activity in the brain.

By way of non-limiting example, a patient may initially receive adiagnosis of seizure activity with a location indicated, e.g., from aclinician. Thereafter, the patient may be asked to monitor for seizureactivity using a sensor 101 b. In an embodiment, a data sourceindicating a diagnosed location of seizure activity is accessed, such asan electronic medical record (EMR) or in an application that receivesuser input to a location map image or illustration. The location datamay be supplied to the patient, e.g., via a companion mobile applicationthat facilitates pairing and data communication between the sensor 101 band a local device. In another example, the location information issupplied to an online application that can be viewed by the user (e.g.,patient or clinician). The location data may therefore be provided tothe user in the form of an instruction that indicates acceptableplacement(s) of the sensor(s), e.g., sensor 101 b, to maximize thelikelihood that a subsequent seizure will be detected using a singlechannel sensor, e.g., sensor 101 b.

Due to the small size of the sensor 101 b, for example on the order ofan inch in length and width, and ½ inch in a depth dimension, the sensor101 b may be worn continuously for a period of days before it needs tobe removed, e.g., for charging an on-board power source such as arechargeable battery. This permits the capture, recording and analysisof a large amount of single channel EEG data using detection processesthat identify seizures, including seizures a user wearing the sensor maynot consciously know they are having, such as seizures that occur whilesleeping.

The sensors 101 a, 101 b illustrated in FIG. 1A and FIG. 1B are suitablefor use in any patient, adult, adolescent, children or newborns. Asingle sensor 101 a, 101 b may be used to facilitate more accuraterecording of EEG data for real-time or later analysis. The sensor isalso waterproof or water resistant, making it suitable for wear duringactivities where the sensor 101 a, 101 b may get wet. This furtherfacilitates long-term wear and comprehensive EEG data collection forseizure diaries, seizure forecasting and seizure alerting.

A consideration in making the sensor 101 a, 101 b a viable, long-termwearable sensor is power consumption. An example target is approximatelythree days of operation without recharging the sensor 101 a, 101 b. Toenable continued monitoring, the users may have two (or multiple)sensors 101 a, 101 b and will use one while the other is beingrecharged. Such an arrangement will allow for continuous EEG datacapturing and monitoring.

To facilitate long term wear, several techniques may be employed. Forexample, a power consuming operation of the sensor 101 a, 101 b istransmitting the EEG data from the sensor 101 a, 101 b to anotherdevice. To reduce the power used for data communication, the sensor 101a, 101 b may transmit the captured EEG data at intervals. For example,the sensor 101 a, 101 b can capture EEG data for a predetermined amountof time (e.g., 6 seconds) and then transmit that captured EEG data,e.g., transmit a page or pages of EEG data. By transmitting the data atintervals, the sensor 101 a, 101 b only needs to activate thetransmission capability for a short time (e.g., 1 second). As anotherexample, a power-efficient microprocessor may be selected for use in thesensor. For example, certain microprocessors may include a sleepprocessor core or capability while transmitting data by DMA to low powerSRAM for data communication. This feature may significantly reduce powerconsumption. Further, sensors 101 a, 101 b may have certain componentsomitted, e.g., wireless radio, and may include other components, e.g.,USB or other data communication element, such as near-field or RFID, ina variety of combinations, to facilitate power conservation and adequatedata transfer for the given use case. In a case where a physicalcommunication port is included, it may be covered to prevent water orcontaminating element entry, such as locating it beneath a removablehydrogel or sticker that adheres the sensor 101 a, 101 b to the user'sskin. The thickness of the sticker may be modified, e.g., the thicknessof the sticker or materials thereof (e.g., hydrogel areas) may beincreased to accommodate the use context, such as placement on skin thatcurves, whereas a thinner sticker may be used on a relatively flatsurface.

The sensors 101 a, 101 b are suitable for use by adults, adolescents,children and neonates. In the example of FIG. 2, a system for monitoringin a clinical or emergency care setting is illustrated.

The example of FIG. 2 uses an example patient; however, thisnon-limiting example may be extended to other clinical or non-clinicalscenarios. Seizures are common in emergency care scenarios orasphyxiated neonates (particularly within the first two days of life).Full-montage clinical EEG systems use many (eleven or more) tethered(wired) electrode leads for monitoring. These leads must be positionedby an EEG technician and can take up to 60 min to place. A reduced set(3-lead) amplitude integrated electroencephalography (aEEG) recordingsystem provides real-time EEG from two channels along with a history ofEEG activity displayed as a filtered, rectified, and averaged signal.However, electrodes are conventionally placed by a specialist. The aEEGleads are placed in the bi-parietal region of the standard 10-20 EEGsystem. An aEEG can be used to diagnose seizures as well as otherbackground EEG abnormalities associated with encephalopathy. Apersistently abnormal aEEG for as little as 48 hours is associated withan adverse neurodevelopmental outcome.

In FIG. 2, an embodiment is shown in which multiple single-channelsensors (collectively indicated at 201) are placed on a patient's scalp,spaced from one another to be approximately over the eyes in thebi-parietal region and behind each ear for creating an EEG montage.Alternatives are possible, for example, two sensors may be placed overroughly the bi-parietal region in the 10-20 EEG system to create threechannels: (1) C3-P3, (2) C4-P4, and (3) a hybrid of C3P3-C4P4. Theoutput of the sensors 201 is synchronized and organized by anapplication (as further described herein) and may be viewed both inreal-time and converted, e.g., to aEEG, by software.

As with a post-diagnosis instruction, an instruction for positioning thesensors 201 may be provided using a device 202 such as a desktopcomputer, tablet, other hospital monitor or mobile device, which runs anapplication 203 and displays an instruction as a graphic indicatingplacement information for the sensors 201 on the patient's scalp, e.g.,on the forehead, behind the ears, a combination thereof, or other oradditional locations. The placement information in this case may begeneralized, e.g., approximate location for seizure monitoring inpatients thought to have suffered a given type of brain injury ortraumatic event, or may be customized in some fashion if access toadditional data is available, e.g., specific type of incident suspectedfor the patient or in another clinical scenario, such as for an adult orpediatric emergent care patient. By way of specific example, anembodiment may provide a graphical instruction such as that illustratedin FIG. 2, where an emergent care screening is to be conducted on apatient using four sensors, two on the forehead and two behind the ears.With this four-sensor arrangement, an embodiment may create a desiredmontage (as described herein), e.g., via subtracting the EEG signal fromone sensor relative to another to create a 10-channel“longitudinal-transverse” montage, as further described in connectionwith FIG. 4B.

In the example illustrated in FIG. 2, an application 203 runs on thedevice 202, e.g., medical grade tablet. The application may beprogrammed to facilitate collecting EEG data for emergent care, althougha version of the application may be used for home use in a seizure diarycontext. In one example, the application 203 provides instructions forEEG data collection by non-experts, such as clinicians in a local orrural hospital unaccustomed to EEG monitoring. By way of example, theapplication 203 may provide a graphic such as illustrated in FIG. 2 thatindicates placement and orientation of four single-channel sensors 201.In some examples, the single-channel sensors 201 may be directional andkeyed, such as via inclusion of a marking. In the example of FIG. 2, thegraphic illustrates gold dots 207 that correspond to similar markings onthe single-channel sensors 201, which allow correct orientation of theelectrodes on the underside of the device (not shown) for placement onthe patient's scalp. That is, a keyed device permits the user toappropriately align the single-channel sensors 201 on the patient.

The instructions and wireless single-channel sensors permit rapidcollection and analysis (even remote analysis) in the field or in aclinical setting by non-experts. This avoids or reduces the need totransport the patient to another location, such as a larger hospitalwith conventional EEG monitoring equipment and specialists. For example,upon seizure suspicion, emergency department staff in a communityhospital can place four sensors 201, as instructed by the graphicillustrated in FIG. 2, on the scalp and below hairline. The sensorsbegin transmitting EEG data to a tablet or other device, which may bethe same device that displays the graphic, i.e., 202 in the example ofFIG. 2. The tablet or other device 202 then relays the EEG data andpatient information to a secure cloud server 204, e.g., running an EEGreviewing platform software. The emergency department staff then ordersa neurology consult with the tertiary hospital EEG service either withinor outside of their hospital network. The epileptologist on call at thetertiary hospital logs on to a mobile application 206 running on adevice 207 to review the EEG in real time or substantially in real time,while also being able to use the quantitative EEG analysis features inthe EEG reviewing platform. Other or additional data may be similarlyprovided, e.g., by other sensors or devices, such as images or video ofthe patient captured with a camera, heart rate, pulse oximetry, ortemperature data captured by suitable devices, etc. An embodiment mayprovide an alert or forecast based on the EEG data, e.g., preliminarydiagnoses, suggested options such as transport patient, continuation ordiscontinuation of pharmaceutical treatment or intervention, etc., orsimply indicate areas in the EEG trace data where a seizure or other EEGabnormality is suspected.

In one example, the application 203 connects to the sensors 201 throughBLE, receives the EEG data, buffers the EEG data, and transmits the EEGdata over WIFI to the cloud 204, where it may be retrieved and viewed byanother clinician in a remote location. For example, the EEG data may beretrieved from the cloud 205 and reviewed by a remote specialist on amedical grade tablet or other device 205. This facilitates review of theEEG data as displayed in an application 206 running on the device 205.EEG data from both sensors 204 may be synchronized, e.g., by the BLEcommands from the tablet 202.

The cloud device 204 may provide a server instance running EEG-reviewsoftware that allows a specialist such as a neonatologist or pediatricepileptologist to log on to a tablet 205 running a mobile application206 to view the EEG in real-time and as aEEG. The aEEG data may includeindications, such as a marking described in connection with FIG. 4A.

The general principals of the example in FIG. 2 may be extended to otherscenarios. For example, in an embodiment for intensive care in pediatricand adults, two sensors, four sensors, eight sensors, or variouscombinations of sensors may be used. Data flow and operations aresimilar to the example of FIG. 2, and an adult epileptologist orpediatric epileptologist can log on to a device 205 to monitor EEG datain real-time, enabling them to make more accurate decisions faster,advising community hospital staff on appropriate care. Similarly, for atriage use case, four sensors in roughly F7, F8, TP9, and TP10, as inthe example of FIG. 2, approximation of the international 10-10 systemgives a total of 8 electrodes producing 10 channels of EEG, as furtherdescribed in connection with FIG. 4B. In an embodiment, the EEG dataobtained by various single-channel sensors may be synchronized andcombined to create desired differential montages. This may beaccomplished at least in part an application or software program, e.g.,that determines a differential montage available (e.g., 10-10) given thenumber and placement of the sensors, displays this to a user, andpermits a user to select a desired configuration or automaticallyconfigures the montage for the user.

In an embodiment, an application, e.g., running on device 202, visuallyguides a user, e.g., emergent care staff, with step-by-step visuals.This may include but is not necessarily limited to guiding the userthrough scanning a barcode on a patient bracelet and each sensor,placing sensor(s) on the scalp, and ensuring quality signals are beingrecorded and relayed, e.g., to the cloud. In some embodiments,additional or reduced data may be provided. For example, EEG data maynot be shown at the point-of-care, e.g., at device 202. This may be doneto accommodate emergency room staff and doctors, which may consider suchdata display to be a distraction. In other embodiments, such data may bedisplayed, e.g., in connection with an alert to a specific action suchas a seizure type detection, a suggested treatment or transport option,etc. Therefore, in an embodiment, the application running on device 202may be designed to only interact with the user when there is a problemin the form of a user alert, such as poor signal quality coming from anindividual sensor or the like. These user alerts are designed toindicate, e.g., flash an LED red or blue on each sensor, to staff thatinteraction with the application is required to guide them throughsolving a problem, e.g., obtain guidance to relocate or reorient asensor as described in connection with FIG. 6.

Collection of EEG data with one or more single-channel sensors allowsEEG data to be reviewed, along with event markers (as further describedherein) to quickly determine areas within the EEG data that areindicative of seizure. As further described herein, the type and natureof detection, analysis or classification the EEG data is subjected tomay change depending on the use case or desired outcome. For example,for triage event marking, a model with higher false positive rate may beemployed as compared to a use case in which real time seizure predictionis desired. Likewise, for in-home seizure diary use, a simplethresholding process may be suitable for producing seizure counts andmarkers on EEG data traces.

Referring to FIG. 3, an example of EEG monitoring and indicating isillustrated. Often it is difficult to diagnose a seizure disorderthrough short-term or even long-term monitoring in an epilepsymonitoring unit with video-EEG. Furthermore, it is estimated that 20-30%of people seen in epilepsy centers are diagnosed with psychogenicnon-epileptic seizures (PNES). Therefore, a mechanism to facilitate longterm monitoring, e.g., home seizure monitoring, is desirable.

In an embodiment, EEG data is obtained from a single channel EEG sensorat 301. As described herein, this may be a single sensor that is placedpost-diagnosis, a single sensor that is placed pre-diagnosis, ormultiple sensors used in a variety of contexts. Single sensor usage maybe more appropriate for in home usage, whereas multiple sensors may bemore appropriate for clinical or supervised scenarios. Each such sensorprovides single channel EEG data.

The single channel EEG data is classified at 302. The classificationperformed at 302 may be implemented using a variety of devices. Forexample, the classification at 302 may be performed by the sensor, at alocal device communicating with the sensor via a PAN, at a remote orcloud device communicating with the sensor via a WAN, or a suitablecombination of the foregoing.

The classification performed at 302 may take a variety of forms. Forexample, a detection model may take the form of simple thresholding todetect brain activity over a certain amount or duration for generalseizure detection. One or more models may be designed to detect specifictypes of brain activity known to be useful in specific clinicalsettings, e.g., emergent care settings as described in connection withFIG. 2. For example, many signal processing techniques have been studiedover the past 40 years to analyze and extract information from capturedEEG data. The signal processing techniques can be used to discriminatebetween ictal (seizure) and inter-ictal (non-seizure) states and thereare a breadth of scientific papers and studies describing various signalprocessing techniques that can be applied to EEG data. The most commonpieces of information extracted from the EEG data are spike-waveoccurrence, time-domain characteristics (e.g., range, variance, skew),frequency-domain characteristics, time-frequency-domain characteristics(e.g., wavelet decompositions), complexity measures (e.g., entropy,fractal dimension), correlative measures (e.g., cross-channelcorrelations), and state dynamics.

An embodiment uses machine learning techniques for use with singlechannel EEG data at 302. In an embodiment, a machine learning model maybe trained using EEG data from one or more sensors, e.g., sensor 101 a,in combination with other EEG data, such as collected via a wired ortethered EEG system. By way of example, two-second segments ofinter-ictal (pre-seizure) and ictal (seizure) EEG data may be extractedfrom the sensor recordings and used to identify seizure occurrences, asfurther described in connection with FIG. 5.

In an embodiment, in identifying ictal states by classifying the EEGdata at 302, correlation(s) with additional data, such as historicaldata (e.g., a pattern or trend, medical record information obtained froman EMR, etc.), environmental data (e.g., weather data), or a user'sactivity (e.g., behavioral) may be used to assist in determining theonset of seizure activity or indicating its past occurrence, asindicated at 304. For example, psychological, behavioral, andenvironmental cues can be identified as potential informationcorrelative to the user's ictal (seizure) state. This additional datamay optionally be used to make a classification of the EEG data orimprove the confidence of an independently made classification.

By way of example, user feedback may be used to improve a seizuredetecting process. Initially, an automated detection of general seizureactivity may be performed at 302. If an embodiment detects generalseizure activity, user supplied input may be used to improve theaccuracy of the detection (e.g., with respect to time, severity or thelike). For example, an input interface, such as a small button includedon the sensor 101 a or an input element included in a mobileapplication, may be used to provide an indication when a user feels thatseizure activity is occurring, is about to occur or just has occurred.Likewise, an interface may allow the user to record the severity,duration, or other data regarding the event. This feedback issubjective, and it may not be desirable to use as a reliable source todetermine an occurrence of seizure activity. However, user supplied datamay provide insight into the user's experience of non-epilepticseizures. Therefore, it may be used to confirm a detected seizure orlack of detection. For example, repeated indications by a user that aseizure is occurring where EEG data is recorded with high quality and anautomated analysis indicates no seizure may indicate that a user isexperiencing something else, e.g., a non-epileptic seizure,psychological event. Conversely, such data feedback may indicate thatmodel or threshold tuning is needed or desirable.

After the EEG data has been classified at 302, e.g., as nominal (e.g.,non-seizure, pre-seizure) or abnormal (e.g., pre-seizure or seizure), anembodiment may provide outputs in the form of indications. In anembodiment EEG data, such as pre-seizure EEG data, may be classified asnominal or abnormal depending on the feature(s) being used forclassification, the context (e.g., a mode may be selected wherepre-seizure activity is ignored and classified as nominal in favor of alower false positive rate, etc.), a varied threshold, etc. In anembodiment, if a seizure is detected as a result of the classificationat 302, this classification may be used at 303 to provide an indicationat 306 such as generating an alert (e.g., to the patient or aclinician), marking the EEG segment that triggered the detection or thatis correlated with additional data, incrementing a seizure count, orforming a report.

By way of specific example, and referring to FIG. 4A, an embodiment mayprovide an indication at 306 that takes the form of a marking of asegment of the EEG data as displayed in a trace 401 a. The indication402 a may highlight the region of the EEG trace that triggered theclassification of a seizure event. This may facilitate review by anepileptologist or another clinician. For example, color coding on thetrace or text or other graphical indicator may be automatically suppliedto facilitate identification of important or interesting portions of theEEG trace 401 a. Additionally, or alternatively, an automated programmay provide a link or position marker to navigate to this portion of theEEG trace 401 a, e.g., automatically or in response to manual input.This will facilitate quick review of large amounts of EEG trace data 401a, e.g., where the patient is continuously wearing the sensor andproviding a large amount of EEG data, such as over several days, to aremote clinician that wishes to quickly review important events atperiodic intervals.

The EEG data of two or more sensors may be displayed in various manners.As illustrated in the example of FIG. 4B, individual sensor EEG tracedata may be displayed as well as differential EEG data obtained viacomparison to another sensor. For example, in FIG. 4B, four sensors,such as four single channel EEG sensors similar to sensor 201 have beenused to record EEG data at locations approximate to F7, TP9, F8 andTP10. These locations may correspond to the forehead (front left andright) and behind the ear (left and right) locations, respectively,utilized in an emergent care setting, as described herein. In theexample of FIG. 4B, the four channels of EEG trace data from theassociated sensors are listed from top to bottom. Differential traces,e.g., F8-TP10, F7-F8, TP9-TP10, F7-TP10, and F8-TP9 are listedthereafter. In an embodiment, the ordering of these EEG traces may bemodified, e.g., based on user preference (identified through user inputsuch as drag and drop of the traces) or via creating more or lessdifferential traces or individual sensor traces. By way of specificexample, in a scenario where two sensors are used, the two sensortraces, e.g., F7 and F8, may be displayed, and one or more differentialtraces created from these sensor's readings may be displayed as well. Inan embodiment, the creation or display of the traces may be automated,e.g., via an application such as described in connection with FIG. 6 andFIG. 4B determining sensor location(s) and automatically associatingsensor pairs to create differential traces of interest.

Referring back to FIG. 3, where the classification at 302 results in anabnormal classification, e.g., pre-seizure, an embodiment may provide anindication at 305 in the form of a forecast or prediction. In oneexample, a pre-seizure classification may occur where the EEG data isabnormal, such as EEG data in which frequency and/or amplitude changesexceed threshold(s) obtained from a nominal EEG data trace, but notsufficiently so to be confidently classified as a seizure event.Similarly, a pre-seizure event may occur where the EEG data matches aknown pattern that leads up to a seizure, such as a pattern indicating acharacteristic frequency of change in the EEG data or a characteristicamplitude change in the EEG data, or a combination thereof, that comesbefore a seizure. As described in example of FIG. 5, features of EEGdata that may be useful in identifying such EEG trace data may beobtained from human labeled training data. As with otherclassifications, this determination may be aided by reference toadditional data 304, such as psychological, behavioral or environmentaldata.

The indication provided at 305 may include a forecast provided to theuser, e.g., the wearer of the sensor. The indication provided at 305 maytake the form of a real time forecast (e.g., produced within a second ortwo) that changes as the EEG data or other data 304 changes. Theindication provided at 305 may take other forms, e.g., an hourly, daily,weekly or other time period forecast. Time period forecasts may beinfluenced by historical data accessed at 304, e.g., an increasing ordecreasing trend in seizure frequency may assist in forming or modifyingthe forecast. The forecast may take a variety of forms, for example ascore or a color displayed in a mobile application that relates to alikelihood of seizure during a time period, e.g., imminent, likelihoodon a day, during the coming week, etc. Similarly, the forecast may takethe form of a haptic, audio or visual effect produced by the sensor or aconnected local device, remote device, etc. The forecast may also beprovided to other or additional users, such as a clinician or anotheruser. In the case of a forecast provided to a clinician, the forecastmay include an indication of a related diagnosis, such as hypoxicischemic encephalopathy, and a related action, such as a suggestedtreatment, e.g., therapeutic hypothermia, or an automated orsemi-automated action, such as requesting a consultation with an on-callspecialist.

Real-time or imminent seizure forecasting or prediction adds additionalcomplexity in that discrimination must be made in a time sensitivemanner, between an inter-ictal (no-seizure coming), a pre-ictal (seizureevent will happen in the next X min/hours), and an ictal (seizure isoccurring) states. Similar to seizure detection, information featuresand machine learning techniques have been widely tested and detailed inscientific literature with respect to seizure forecasting. Seizureforecasting success is often measured by sensitivity (was a warningcorrect that a seizure was coming and were any missed) and by either afalse alarm rate per hour or by a “time-in-warning” (how often dowarnings occur). Not everyone with epilepsy can identify apsychological, behavioral, or environmental seizure precipitatingfactor. Yet, over half of all people with epilepsy report at least oneseizure precipitating factor. The number one seizure precipitatingfactor is emotional stress. This is followed by behavioral factors wellknown to trigger seizures, such as sleep deprivation and tiredness.Other behavioral factors include alcohol consumption, anti-seizuremedication non-adherence, and physical exercise. Environmental factorssuch as time-of-day, flickering light, and weather (e.g., ambienttemperature, relative humidity) have been shown to increasesusceptibility to seizures.

As with seizure detection, seizure prediction may take the form of aclassification performed at 302. Likewise, additional data such ashistorical, environmental or user provided data may be used to generateor modify the classification of the EEG data as nominal or abnormal.This data may be used to form forecasts or predictions provided at 305.

The additional data used to classify at 302 may include but is notlimited to historical data (e.g., seizure trend data, etc.),environmental data (e.g., weather, stimulus data such as exposure toflickering light, etc.), and user data (e.g., behavioral data). The userdata may be provided by the user directly or indirectly. For example,the user data may be input by the user directly, such as entering inself-evaluation data to a mobile application. Non-limiting examplesinclude stress level, sleep quality rating, sleep score according to aknown scale, sleep time, etc. User or other data may be obtainedindirectly, e.g., from a linked health app, from a medical record, frominput of another user on another device, such as a physician, orinferred from another device such as a mobile phone or smart watchproviding accelerometer data, etc.

In an embodiment, an indication may take the form of a report, asindicated by way of example at 306. For example, a digital seizure diaryreport may be provided to the patient or clinician. Epileptologists willhave a precise, quantitative record of a patient's seizure activity andthat will let them know if a treatment is working, enabling them toadapt the patient's treatment more rapidly and successfully.

The improvements to a seizure detection or prediction are heavilyreliant on the volume and quality of EEG data collected. Currently thereexists no practical EEG database. While there are some laboratory EEGdatabases (e.g., MIT database), the EEG data in these databases are tooclean to use for prediction as they are not representative of thequality of EEG data that will be collected in the real world. Ease ofthe EEG data collection will improve EEG data availability, such as viathe of use of the EEG data collection sensor 101 a,101 b and its minimalimpact on the user's everyday routine. Hence, a feature of an embodimentis use of a single channel EEG data collection sensor 101 a, 101 b,accompanying EEG data analysis, and seizure prediction techniques(s).While a single channel EEG data collection may not be able to accuratelyidentify where in the user's brain seizure activity is occurring all thetime or initially, the simple detection of the occurrence of seizureactivity presents a valuable tool to help users manage and treatepilepsy. As described, the location or placement of the sensor may berefined over time, e.g., in connection with a preliminary or subsequentfull EEG montage, in connection with analysis of the sensor's dataquality, etc. Additionally, predictive capabilities may provide userswith improved quality of life as they can conduct activities withreduced anxiety that an unexpected seizure may occur.

One aspect of identifying and/or predicting seizures via classificationat 302 may include discriminating between the various seizure types. Forexample, the absence seizure typically occurs many times a day and theelectrographic signature of such a seizure is the same across all ages.Therefore, it may be comparatively easier to collect extensive EEG dataand improve a seizure prediction model by machine learning to detectabsence seizures. The other type of seizures may occur less frequently,such as once a month, so they may be difficult to predict accurately dueto the lack of EEG data on which machine learning models can be trained.However, starting from the creation of a generalized seizure predictionmodel for a common seizure type, an embodiment may be expanded andrefined by use of a model that covers other types of seizures,particularly as long-term wear of the sensors 101 a, 101 b by the userscontinues. This EEG data may be stored and used with permission of theusers to build a database suitable for forming future seizure detectionand prediction models.

Turning now to FIG. 5, an example of EEG data classification isillustrated. The example classification technique shown in FIG. 5 may beused to provide a classification as part of a larger processingtechnique, for example that shown in FIG. 3.

In an embodiment that utilizes a machine learning process to classifythe EEG data, a training phase may include processes outlined in 501-505of FIG. 5. By way of example, as shown at 501 a patient wears aplurality of sensors, e.g., four single channel EEG sensors, such assensor 201 of FIG. 2. In one example, four sensors 201 may be arrangedon forehead and behind the ear positions, e.g., one sensor on the leftforehead, one sensor on the right forehead (F7/F8), one sensor on theleft behind the ear and one sensor on the right behind the ear(TP9/TP10). The sensors may be worn for a period of time to collecttraining EEG data, e.g., a patient may wear the sensors 201 for sevendays during an Epilepsy Monitoring Unit (EMU) stay.

In one example, the training data may include both single channel EEGdata collected using sensors 201 as well as EEG data collected using anormal 10-20 or 10-10 multi-channel wired EEG (wired EEG) sensor orheadset as part of standard of care. That is, both the sensors 201 andthe wired EEG sensor or headset may be worn by the same patient at thesame time to obtain a set of EEG training data.

During an EMU stay, one or more of reviewing software and anepileptologist identifies potential seizure events in the wired EEG datarecord. Patients and/or family in the room may also indicate, e.g., pusha button, that a seizure is occurring. An epileptologist reviews theentire multi-day wired EEG, along with the reviewing software and userprovided event markers, to determine when a seizure occurred (as isconventional). An epileptologist may also review the EEG data toidentify what type of seizure event occurred (as is conventional). If aseizure was focal in origin, an epileptologist may indicate which wiredEEG electrodes was center of focus. An epileptologist may also indicateEEG start/stop of seizure, whether the electrographic seizure is visibleon the wired EEG at locations where each sensor was placed, andelectrographic obscuring artifact(s) (such as patent movement)start/stop. From this information, when a seizure should beelectrographically visible (per conventional techniques) is known andthis may be utilized to compare with the data obtained via the foursensors 201.

Indicated at 502, a pre-processing of raw EEG data is performed. By wayof example, noise removal or filtering may be applied, such as removalof 50/60-Hz line noise, low-pass filtering to remove electromyographic(EMG) muscle activity, normalization of standardization to account forinter-patient and inter-sensor differences in the data amplitude. Otheror additional signal processing conducted at 501 may includeelectro-ocular artifact rejection (to remove the impact of eyemovement), e.g., from certain sensor placements such as F7/F8 placedsensors.

Pre-processed data is segmented into short-duration segments (forexample between 0.5-10 seconds) at 503. In an embodiment, each segmentis labeled, e.g., as seizure or non-seizure, based on its origin from atime previously noted as during a seizure in the patient wearing phaseof 501 and if the seizure was visible at the sensor 201 location(s).

Feature Extraction is performed at 504. In an embodiment, one or more ofthe following features are extracted from the segmented data: timedomain (min, max, mean, median, range, variance, standard deviation,skew, kurtosis); frequency domain (Fast-Fourier Transforms, EEG specificbands (s, delta, theta, alpha, beta, gamma)); time-frequency domain(wavelets); complexity domain (sample/spectral entropy, non-linearenergy operator, Hjorth parameters, fractal dimensions); transforms:(principal component analysis, linear discriminant analysis); andhistorical (past segment values, which may be weighted).

Model tuning is performed at 505. Because the EEG sensor data ishighly-imbalanced (e.g., 100:1 ratio of non-seizure segments toseizure), a subset of the EEG data may be used (e.g., 3:1 for the EEGdata/model). A machine learning model, for example a random forest asshown in FIG. 5 or a support vector machine, an artificial neuralnetwork (shallow or deep), etc., is trained and tuned on the trainingdata at 505. For example, tuning may include hyper-parameter tuning,feature relevance determination, cross-validation (e.g., usingleave-one-out (LOO) methods), etc.

Metrics such as receiver operating curve (ROC) area under the curve(AUC), specificity, sensitivity, positive predictive value, falsepositive rate, or any combination of foregoing, may be used to determinethe best model. In certain cases, e.g., where seizure detection isparamount and false positives are tolerable, e.g., in a seizure diarycontext, a particular model may be chosen over use in another scenario,e.g., where false positives are to be minimized, such as automatedmedication recommendations or providing suspected diagnoses. In anembodiment, the model employed may be exchanged or modified, e.g., byadjusting a parameter such as a probability threshold, to suit the usecontext. By way of example, an embodiment may adjust the model employedby offering the end user data input interfaces, such as displayingselectable elements that indicate the use context, which after selectionloads a predetermined model or set of parameter(s) for the contextindicated. For example, contexts such as seizure diary, real timealerting, emergent care, etc., may be indicated via selections, whichloads a model or adjusts a model's parameter(s), e.g., probabilitythreshold(s) that can be modified to adjust sensitivity, to match thecontext indicated. More experienced users may interface with the modelparameters more directly.

Following the tuning at 505, the tuned model(s) may be saved for use indetection. For example, in steps 506-510, a model such as a previouslytuned model is accessed and run on patient data, e.g., collected usingone or more sensors 201 in a treatment scenario. As described herein,this may include a process of segment detection, as outlined in 506-510.By way of example, at 506 a patient, e.g., that has been previouslydiagnosed with a seizure disorder, wears one or more sensors 201, e.g.,using a placement guided by the patient's epileptologist as to be themost likely to pick up seizure events. No wired EEG data would berecorded and the patient wears the sensor(s) 201, e.g., during everydayactivities. In one example, the patient wears the sensor(s) 201 up to24-hours a day and may do so for several days.

EEG data collected by the sensors 201 may be streamed into a remotedevice such as a cloud-platform, as per the example of FIG. 2. Rawsensor EEG data pre-processing is performed at 507, EEG segments areidentified at 508, and feature(s) extracted at 509, which may be similarto the processes performed at 502, 503, and 504, respectively.

At 510, the unlabeled, segmented, feature set is run through the trainedmodel, the outputs of which may be utilized in a whole seizure detectionprocess, as for example outlined at 511-514.

In the example of FIG. 5, the outputs of the model run at 510 mayinclude segment probabilities 511 for seizure events per segment or setof segments. In one embodiment, the output of the model run at 510 is a0-1 likelihood that the segment occurs during a seizure event. In someembodiments, the specific type of seizure may or may not be determined,e.g., if a machine learning model tuned for a specific seizure type isemployed, such as via user selection of such a model. In an embodiment,a general seizure detection model and identification process, e.g., asoutlined in 510-514, is akin to a series of models with probabilitiesfor each seizure type, combined or considered together to make aseizure/non-seizure determination.

At 512 segment stitching is performed. For example, segmentprobabilities are combined to create a start/stop time for a (whole)seizure event, i.e., consisting of multiple segments. This may beaccomplished in many ways. By way of example, the segments may bestitched or combined together via individual segment thresholding (suchas comparing frequency and amplitude EEG data changes to threshold(s)for each segment), a multi-segment thresholding and windowing process(combining or considering together multiple segments probabilities,e.g., in comparison to one or more thresholds), or integration windowing(e.g., weighted, leaky, etc.). The windows are typically as short as afew seconds (e.g., absence seizures) or up to minutes in duration (e.g.,for focal seizures).

Annotations are generated, e.g., for a patient medical record or EEGtrace display, for the start/stop time of the determined seizure eventsat 513. This annotation list data may be utilized in a variety of ways,e.g., as an indication per FIG. 3. For example, an embodiment maypresent an annotation list (list of segments of EEG data that areassociated with a seizure or metadata for identifying such EEG data)directly to the patient through an application run on a local device, ordirectly to the clinician via a local or remote device (e.g., if theclinician is remote). The annotation list data may also be stored withthe raw EEG trace data in a cloud-platform for clinician review.

Indicated at 514 is an epileptologist review stage. In an embodiment,clinicians may review the annotations and/or the raw EEG data and makeclinical decisions. The process of epileptologist or simply user reviewat 514 may be influenced by the context. For example, in an embodiment,a low-threshold may be set (as described herein) that leads to manyfalse positives. In such an embodiment, a whole seizure/no-seizuredetermination may or may not even be made. Rather, the annotationsproduced at 513 may be provided to a clinician, e.g., for more rapidreview of potentially interesting EEG data, and seizure/non-seizuredecisions may be made by the clinician based on this review. In othercontexts, the review may be conducted by another, e.g., an at home usermaking a seizure diary. In this context, additional or different datamay be displayed, e.g., time or location context data indicating whenthe potentially interesting EEG data was recorded to facilitate userreview.

Referring to FIG. 6, to facilitate use of one or more EEG sensors suchas sensor 201 by non-expert users, an embodiment provides anapplication, such as a mobile application for use on a mobile device,that guides the user in placing the sensors and recording EEG data. Byway of example, as illustrated in FIG. 6, an embodiment permits the userto easily capture sensor or patient data using the mobile device. In theexample of FIG. 6, at 601 the application obtains sensor data, forexample via capturing an image of a bar code, QR code, or other codeddata, such as for example provided with each sensor. This permits anembodiment to automatically identify the sensor. Similarly, patient datamay also be automatically or semi-automatically captured by theapplication. Of course, as will other data inputs described herein, thedata may be entered manually. An example screen or application view of auser capturing sensor data from a bar code is provided in FIG. 7A. Asillustrated in FIG. 7A, the application may capture an image of the barcode and automatically populate the display screen with the capturedsensor information (e.g., an identification formed from the bar code orother captured data). The application may further indicate to the userhow many sensors are to be used for the application or context. In theexample of FIG. 7A, four sensor locations are indicated, two of whichhave been successfully identified. This assists the user in determininghow many sensors are to be used in the scenario, e.g., four sensors foran emergent care scenario.

In an embodiment, an application may further display instructional stepsto the user. For example, the application may display instructions forturning on or activating the sensor, pairing a sensor, which may be anautomated or semi-automated routine accomplished with a user input suchas a button press, confirming that a sensor is connected to the mobiledevice, confirming that the mobile device is connected with a remotedevice (e.g., cloud platform), preparing a sensor to be adhered to apatient, determining the appropriate location(s) for sensor placement onthe patient, re-positioning the sensors in the application, recordingdata, and storing data (locally or remotely). Further, the applicationmay include additional or alternative display capabilities, e.g., theability to have a live video call with an expert, clinician, etc., thelatter of which may assist in live or real-time troubleshooting ordiagnosis contexts.

Once the user has activated and connected the sensor(s) to the mobiledevice running the application, this is confirmed by the application asillustrated at 602. For example, the application may display thesensor(s) in a location illustration, as indicated in FIG. 7B. Thisassists the user in determining if the sensor(s) are properly located onthe patient for the given context and this is accurately reflected inthe application view. In the example of FIG. 7B, the sensors are to belocated in forehead and behind the ear positions, as illustrated. If agiven sensor ID in the illustration provided by the application does notmatch the actual location of the physical sensor on the patient (e.g.,visible on the bar code or otherwise identifiable on the physicalsensor), a user may select (e.g., touch in the case of a mobile touchscreen) the subject sensor icon to reassign its location in theillustration. By way of example, if sensor 502 of FIG. 7B is illustratedby the application as a right-front located sensor, but in reality, itwas placed on the left-front by the user, the user may simply relocateit by interfacing with the application. This may take a variety of ways.In the example of FIG. 7B, a user may touch a selectable icon, one ofwhich is indicated at 702 for sensor 502, to bring up a menu forswapping its position with another sensor in the array, as indicated at701. Similarly, another mechanism such as drag and drop of the icon 702may be used to reposition the sensor(s) in the application. This ensuresthat the EEG data collected by the actual sensors is known to theapplication, e.g., for creating differential EEG data via subtractionfrom another sensor location.

Referring again to FIG. 6, an embodiment may confirm that the sensorlocations are appropriate at 603, following which, if no decision torelocate the sensor(s) is forthcoming, as indicated at 604, a recordingsession may begin, as shown at 605. At a predetermined time or based onanother factor such as user selection or interface, the recordingsession may be concluded, as determined at 606. Thereafter, the EEG dataof the session may be stored locally, remotely, or both, as indicated at607. As described herein, during the recording at 605, other activitiesmay be performed by an embodiment. For example, the EEG data of thesensor(s) may be analyzed locally or remotely, e.g., by a cloudplatform, a remote clinician, etc.

It will be readily understood that certain embodiments can beimplemented using any of a wide variety of devices or combinations ofdevices. Referring to FIG. 8, an example system on chip (SoC) includedin a computer 800 is illustrated, which may be used in implementing oneor more embodiments. The SoC or similar circuitry outlined in FIG. 8 maybe implemented in a variety of devices in addition to the computer 800,for example similar circuitry may be included in a sensor 870 or anotherdevice or platform 870 a. In addition, circuitry other than a SoC, anexample of which is provided in FIG. 8, may be utilized in one or moreembodiments. The SoC of FIG. 8 includes functional blocks, asillustrated, integrated onto a single semiconductor chip to meetspecific application requirements.

The central processing unit (CPU) 810, which may include one or moregraphics processing units (GPUs) and/or micro-processing units (MPUs),includes an arithmetic logic unit (ALU) that performs arithmetic andlogic operations, instruction decoder that decodes instructions andprovides information to a timing and control unit, as well as registersfor temporary data storage. The CPU 810 may comprise a single integratedcircuit comprising several units, the design and arrangement of whichvary according to the architecture chosen.

Computer 800 also includes a memory controller 840, e.g., comprising adirect memory access (DMA) controller to transfer data between memory850 and hardware peripherals. Memory controller 840 includes a memorymanagement unit (MMU) that functions to handle cache control, memoryprotection, and virtual memory. Computer 800 may include controllers forcommunication using various communication protocols (e.g., I²C, USB,etc.).

Memory 850 may include a variety of memory types, volatile andnonvolatile, e.g., read only memory (ROM), random access memory (RAM),electrically erasable programmable read only memory (EEPROM), Flashmemory, and cache memory. Memory 850 may include embedded programs anddownloaded software, e.g., EEG processing software, etc. By way ofexample, and not limitation, memory 850 may also include an operatingsystem, application programs, other program modules, and program data.

A system bus permits communication between various components of thecomputer 800. I/O interfaces 830 and radio frequency (RF) devices 820,e.g., WIFI and telecommunication radios, BLE devices, etc., are includedto permit computer 800 to send and receive data to sensor(s) 870 orremote devices 870 a using wired or wireless mechanisms. The computer800 may operate in a networked or distributed environment using logicalconnections to one or more other remote computers or databases. Thelogical connections may include a network, such as a personal areanetwork (PAN), a local area network (LAN) or a wide area network (WAN)but may also include other networks/buses. For example, computer 800 maycommunicate data with and between a sensor 870 and remote devices 870 aover the Internet.

The computer 800 may therefore execute program instructions configuredto store and analyze EEG data, and perform other functionality of theembodiments, as described herein. A user can interface with (forexample, enter commands and information) the computer 800 through inputdevices, which may be connected to I/O interfaces 830. A display orother type of device may also be connected or coupled to the computer800 via an interface selected from I/O interfaces 830.

It should be noted that the various functions described herein may beimplemented using executable instructions stored in a memory, e.g.,memory 850, that are transmitted to and executed by a processor, e.g.,CPU 810. Computer 800 includes one or more storage devices thatpersistently store programs and other data. A storage device, as usedherein, is a non-transitory storage medium. Some additional examples ofa non-transitory storage device or medium include, but are not limitedto, storage integral to computer 800, such as a hard disk or asolid-state drive, and removable storage, such as an optical disc or amemory stick.

Program code stored in a memory or storage device may be transmittedusing any appropriate transmission medium, including but not limited towireless, wireline, optical fiber, cable, RF, or any suitablecombination of the foregoing.

Program code for carrying out operations may be written in anycombination of one or more programming languages. The program code mayexecute entirely on a single device, partly on a single device, as astand-alone software package, partly on single device and partly onanother device, or entirely on another device. In some cases, thedevices may be connected through any type of connection or network,including a LAN, a WAN, a short-range wireless mechanism such as a PAN,a near-field communication mechanism, or the connection may be madethrough other devices (for example, through the Internet using anInternet Service Provider), using wireless connections or through a hardwire connection, such as over a USB connection.

Example embodiments are described herein with reference to the figures,which illustrate example methods, devices and program products accordingto various example embodiments. It will be understood that the actionsand functionality may be implemented at least in part by programinstructions. These program instructions may be provided to a processorof a device to produce a special purpose machine, such that theinstructions, which execute via a processor of the device implement thefunctions/acts specified.

It is worth noting that while specific elements are used in the figures,and a particular ordering of elements has been illustrated, these arenon-limiting examples. In certain contexts, two or more elements may becombined, an element may be split into two or more elements, or certainelements may be re-ordered or re-organized or omitted as appropriate, asthe explicit illustrated examples are used only for descriptive purposesand are not to be construed as limiting.

Although illustrative example embodiments have been described hereinwith reference to the accompanying figures, it is to be understood thatthis description is not limiting, and that various other changes andmodifications may be affected therein by one skilled in the art withoutdeparting from the scope or spirit of the disclosure.

What is claimed is:
 1. A method, comprising: obtaining EEG data from oneor more single channel EEG sensor worn by a user; classifying, using aprocessor, the EEG data as one of nominal and abnormal; and providing anindication associated with a classification of the EEG data.
 2. Themethod of claim 1, wherein the indication is one or more of an alert,data marking an EEG trace, a count, a report, and a forecast.
 3. Themethod of claim 2, wherein the providing comprises marking a segment ofan EEG trace of the EEG data, wherein the marking includes providing oneor more of a color code and a label for display on a display device. 4.The method of claim 1, wherein the classifying comprises: evaluating aplurality of single channel EEG time segments individually; identifyinga set of the plurality of single channel EEG time segments to indicate aseizure event lasting longer than an individual EEG time segment; andcreating an annotation list comprising ordered EEG time segments.
 5. Themethod of claim 1, wherein the classifying comprises analyzing the EEGdata in combination with one or more of historical data, environmentaldata, and user supplied data.
 6. The method of claim 1, wherein theobtaining comprises receiving the EEG data at a remote device, whereinthe classifying is performed using the remote device and the indicationis provided to a second remote device.
 7. The method of claim 1, whereinthe obtaining comprises obtaining EEG data of four single channel EEGsensors; each of the four single channel EEG sensors being disposed on apatient at one of a forehead position and a behind the ear position. 8.The method of claim 7, wherein the classifying comprises using a modeltrained using data obtained by one or more of a single channel EEGsensor and wired EEG sensors.
 9. The method of claim 7, wherein theclassifying comprises using a model trained using data obtained by aplurality of single channel EEG sensors worn by a user and data obtainedfrom wired EEG sensors worn by the user.
 10. The method of claim 1,wherein the obtaining comprises obtaining EEG data from two or moresingle channel EEG sensors; the method comprising providing aninstruction for placement of the two or more single channel EEG sensors.11. A system, comprising: an output device; a processor operativelycoupled to the output device; and a memory storing instructionsexecutable by the processor to: obtain EEG data from one or more singlechannel EEG sensor worn by a user; classify the EEG data as one ofnominal and abnormal; and provide an indication associated with aclassification of the EEG data.
 12. The system of claim 11, wherein theindication is one or more of an alert, data marking an EEG trace, acount, a report, and a forecast.
 13. The system of claim 12, wherein theoutput device is a display device, and wherein the instructions areexecutable by the processor to mark a segment of an EEG trace of the EEGdata, including providing one or more of a color code and a label fordisplay on the display device.
 14. The system of claim 11, wherein theinstructions executable by the processor to classify comprise:instructions for evaluating a plurality of single channel EEG timesegments individually; instructions for identifying a set of theplurality of single channel EEG time segments to indicate a seizureevent lasting longer than an individual EEG time segment; andinstructions for creating an annotation list comprising ordered EEG timesegments.
 15. The system of claim 11, wherein the instructions areexecutable by the processor to analyze the EEG data in combination withone or more of historical data, environmental data, and user supplieddata.
 16. The system of claim 11, wherein the output device acts tocommunicate the indication over a network to a remote device.
 17. Thesystem of claim 11, wherein the instructions are executable by theprocessor to obtain the EEG data from four single channel EEG sensors;each of the four single channel EEG sensors being disposed on a patientat one of a forehead position and a behind the ear position.
 18. Thesystem of claim 11, wherein the instructions executable by the processorto classify use a model trained using data obtained by one or more of asingle channel EEG sensor and wired EEG sensors.
 19. The system of claim18, wherein the instructions executable by the processor to classify usea model trained using data obtained by a plurality of single channel EEGsensors worn by a user and data obtained from wired EEG sensors worn bythe user.
 20. A method, comprising: obtaining EEG data from two or moresingle channel EEG sensors worn by a user; transmitting the EEG data toa remote device; classifying, using a processor of the remote device,the EEG data as one of nominal and abnormal; and providing, from theremote device to a display associated with a remote user, datacomprising a montage of the EEG data.